Summer School Courses for Machine Learning and Heliophysics
The theme for the 2025 and 2026 Heliophysics Summer School was Data Driven Heliophysics and focused on the physics derived from observations and new techniques for data analysis, particularly machine learning algorithms. This pathway is a collection of the lectures and activities that were delivered during these summer schools.
Fundamental Topics in Machine Learning
This set of lectures were provided by Farzad Kamalabadi and covers the fundamentals of data analysis and machine learning techniques. The same lecturers were given in both 2026 and 2025. The links provided here are from the 2026 lecture series.
Applications of Machine Learning to Heliophysics
This set of videos provides examples of machine learning applications in heliophysics. The first two lectures provide board overviews of applications, while the last two lectures provide details of specific examples.
Data Analysis and Machine Learning Activities
During the summer school, participants worked together on a number of activities that highlighted data analysis and specific machine learning approaches. These labs were presented as Python Notebooks and include both code and discussion questions.
The code emphasizes readability and transparency over efficiency so that they can be used as both activities, but also building blocks for future projects.